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        <li class="main active"><a href="#continuize">Continuize</a></li>
        
            <li><a href="#signals">Signals</a></li>
        
            <li><a href="#description">Description</a></li>
        
            <li><a href="#example">Example</a></li>
        
    
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<h1 id="continuize">Continuize</h1>
<p><img alt="image" src="../icons/continuize.png" /></p>
<p>Turns discrete attributes into continuous dummy variables.</p>
<h2 id="signals">Signals</h2>
<p><strong>Inputs</strong>:</p>
<ul>
<li><strong>Data</strong></li>
</ul>
<p>Input data set.</p>
<p><strong>Outputs</strong>:</p>
<ul>
<li><strong>Data</strong></li>
</ul>
<p>Output data set.</p>
<h2 id="description">Description</h2>
<p><strong>Continuize</strong> widget receives a data set in the input and outputs the same
data in which the discrete attributes (including binary attributes) are
replaced with continuous.</p>
<p><img alt="image" src="../images/Continuize-stamped.png" /></p>
<ol>
<li>
<p>Continuization methods, which define the treatment of
multivalued discrete attributes. Say that we have a discrete attribute
status with values low, middle and high, listed in that order. Options
for their transformation are:</p>
</li>
<li>
<p><strong>Target or First value as base</strong>: the attribute will be transformed
  into two continuous attributes, status=middle with values 0 or 1
  signifying whether the original attribute had value middle on a
  particular example, and similarly, status=high. Hence, a
  three-valued attribute is transformed into two continuous
  attributes, corresponding to all except the first value of
  the attribute.</p>
</li>
<li><strong>Most frequent value as base</strong>: similar to the above, except that the
  data is analyzed and the most frequent value is used as a base.
  So, if most examples have the value middle, the two newly
  constructed continuous attributes will be status=low
  and status=high.</li>
<li><strong>One attribute per value</strong>: this would construct three continuous
  attributes out of a three-valued discrete one.</li>
<li><strong>Ignore multinominal attributes</strong>: removes the multinominal
  attributes from the data.</li>
<li><strong>Treat as ordinal</strong>: converts the attribute into a continuous
  attribute with values 0, 1, and 2.</li>
<li>
<p><strong>Divide by number of values</strong>: same as above, except that the values
  are normalized into range 0-1. So, our case would give values 0, 0.5 and 1.</p>
</li>
<li>
<p>Define the treatment of continuous attributes. You will usually prefer <em>Leave as it is</em> option. The alternative is 
  <em>Normalize by span</em> which will subtract the lowest value found in the data and divide by the
  span, so all values will fit into [0, 1]. Finally, <em>Normalize by variance</em> subtracts the average and divides by the 
  variance.</p>
</li>
<li>
<p>Define the treatment of class attributes. Besides leaving it as it is, 
  there are also a couple of options which are
  available for multinominal attributes, except for those options which
  split the attribute into more than one attribute - this obviously cannot
  be supported since you cannot have more than one class attribute.</p>
</li>
<li>
<p>With <em>value range</em>, you can define the values of the new attributes. In
  the above text we supposed the range <em>from 0 to 1</em>. You can change it to
  <em>from -1 to 1</em>.</p>
</li>
<li>
<p>If <em>Auto apply is on</em>, the data set is committed on any change.
  Otherwise, you have to press <em>Apply</em> after each change.</p>
</li>
</ol>
<h2 id="example">Example</h2>
<p>The schema below shows a typical use of this widget: in order to
properly plot linear projection of the data, discrete attributes need to
be converted to continuous, therefore we put the data through Continuize
widget before drawing it. Attribute “chest pain” originally had four
values and was transformed into three continuous attributes; similar
happened to gender, which was transformed into a single attribute
gender=female.</p>
<p><img src="images/Continuize-Schema.png" alt="image" width="400"></p></div>
            
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